Approximately 50–75% of aviation's climate impact is caused by non-CO₂ effects, like the production of ozone and the formation of contrail cirrus clouds, which can be effectively prevented by re-routing flights around highly climate-sensitive areas. Here, we discuss options how to incentivize re-routing approaches and apply multicriteria trajectory optimizations to demonstrate the feasibility of the concept of climate-charged airspaces (CCAs). We show that although climate-optimized re-routing results in slightly longer flight times, increased fuel consumption and higher operating costs, it is more climate-friendly compared to a cost-optimized routing. In accordance to other studies, we find that the averaged temperature response over 100 years (ATR ${}_{100}$) of a single flight can be reduced by up to 40%. However, if mitigation efforts are associated with a direct increase in costs, there is a need for climate policies. To address the lack of incentivizing airlines to internalize their climate costs, this study focuses on the CCA concept, which imposes a climate charge on airlines when operating in highly climate-sensitive areas. If CCAs are (partly) bypassed, both climate impact and operating costs of a flight can be reduced: a more climate-friendly routing becomes economically attractive. For an exemplary North-Atlantic network, CCAs create a financial incentive for climate mitigation, achieving on average more than 90% of the climate impact reduction potential of climate-optimized trajectories (theoretical maximum, benchmark). Key policy insights

Aviation guarantees mobility, but its emissions also contribute considerably to climate change. Therefore, climate impact mitigation strategies have to be developed based on comprehensive assessments of the different impacting factors. We quantify the climate impact mitigation potential and related costs resulting from changes in aircraft operations and design using a multi-disciplinary model workflow. We first analyze the climate impact mitigation potential and cash operating cost changes of altered cruise altitudes and speeds for all flights globally operated by the Airbus A330-200 fleet in the year 2006. We find that this globally can lead to a 42% reduction in temperature response at a 10% cash operating cost increase. Based on this analysis, new design criteria are derived for future aircraft that are optimized for cruise conditions with reduced climate impact. The newly-optimized aircraft is re-assessed with the developed model workflow. We obtain additional climate mitigation potential with small to moderate cash operating cost changes due to the aircraft design changes of, e.g., a 32% and 54% temperature response reduction for a 0% and 10% cash operating cost increase. Hence, replacing the entire A330-200 fleet by this redesigned aircraft ( $M{a}_{cr}$ = 0.72 and initial cruise altitude (ICA) = 8000 m) could reduce the climate impact by 32% without an increase of cash operating cost.

The current push in automation, communication, and electrical energy storage technologies has the potential to lift urban mobility into the sky. As several urban air mobility (UAM) concepts are conceivable, all relevant physical effects as well as mutual interrelations of the UAM system have to be addressed and evaluated at a sufficient level of fidelity before implementation. Therefore, a collaborative system of systems modeling approach for UAM is presented. To quickly identify physical effects and cross-disciplinary influences of UAM, a pool of low-fidelity physical analysis components is developed and integrated into the Remote Component Environment (RCE) workflow engine. This includes, i. a., the disciplines of demand forecast, trajectory, vertiport, and cost modeling as well as air traffic flow and capacity management. The definition and clarification of technical interfaces require intensive cooperation between specialists with different areas of expertise. To reduce this communication effort, the Common Parametric Aircraft Configuration Schema (CPACS) is adapted and used as central data exchange format. The UAM system module is initially applied for a 24-hour simulation of three generic networks in Hamburg City. After understanding the basic system-level behavior, higher level analysis components and feedback loops must be integrated in the UAM system module for evaluation and optimization of explicit operating concepts.

While different vehicle configurations enter the AAM market, airlines declare different ticket fares for their operations. This research investigates the operating cost of an airline and the economic viability with the announced fare per km rates. For this purpose, three use cases in the metropolitan area of Hamburg showcase representative applications of an AAM system, whereby a flight trajectory model calculates a flight time in each case. The direct operating cost are investigated for each use case individually and are sub-classified in five categories: fee, crew, maintenance, fuel and capital costs. Here, each use case has its own cost characteristics, in which different cost elements dominate. Additionally, a sensitivity analysis shows the effect of a variation of the flight cycles and load factor, that influences the costs as well as the airline business itself. Based on the occurring cost, a profit margin per available seat kilometer lead to a necessary fare per km, that an airline has to charge.

The European Air Traffic Management Network (EATMN) is comprised of various stakeholders and actors. Accordingly, the operations within EATMN are planned up to six months ahead of target date (tactical phase). However, stochastic events and the built-in operational flexibility (robustness), along with other factors, result in demand and capacity imbalances that lead to delayed flights. The size of the EATMN and its complexity challenge the prediction of the total network delay using analytical methods or optimization approaches. We face this challenge by proposing a deep convolutional neural network (DCNN), which takes capacity regulations as the input. DCNN architecture successfully improves the prediction results by 50 percent (compared to random forest as the baseline model). In fact, the trained model on 2016 and 2017 data is able to predict 2018 with a mean absolute percentage error of 22% and 14% for the delay and delayed traffic, respectively. This study presents a method to provide more accurate situational awareness, which is a must for the topic of network resiliency.